Objective The statistical and computational theory of learning is one of the prime achievements of computer science and engineering. This is evident both in terms of mathematical elegance of capturing intuitive notions rigorously as well as in terms of practical applicability: machine learning has effectively reshaped the way we use information.In this proposal we tackle the very basic notions of learning. Learning theory traditional focuses on statistics and computation. We propose to add information to the characterization of learning: namely the research question we address is: how much information is necessary to learn a certain concept efficiently?The crucial difference from classical learning theory is that traditionally statistical complexity was measured in terms of the number of examples needed to learn a concept. Our question is more finely grained: what if we are allowed to inspect only parts of a given example? Can we reduce the amount of information necessary to successfully learn important concepts? This question is fundamental in understanding learning in general and designing efficient learning algorithms in particular. We show how recent advancements in convex optimization for machine learning yields positive answers to some of the above questions: there exists cases in which much more efficient algorithms exist for learning practically important concepts. Our goal is to characterize learning from the viewpoint of the amount of information necessary to learn, to design new algorithms that access less information than current state-of-the-art and are consequently significantly more efficient. New answers for these fundamental questions will be a breakthrough in our understanding of learning at large with significant potential for impact on the field of machine learning and its applications. Fields of science natural sciencescomputer and information sciencesartificial intelligencemachine learning Programme(s) FP7-IDEAS-ERC - Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Topic(s) ERC-SG-PE6 - ERC Starting Grant - Computer science and informatics Call for proposal ERC-2013-StG See other projects for this call Funding Scheme ERC-SG - ERC Starting Grant Host institution TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY EU contribution € 1 453 802,00 Address SENATE BUILDING TECHNION CITY 32000 Haifa Israel See on map Activity type Higher or Secondary Education Establishments Principal investigator Elad Eliezer Hazan (Dr.) Administrative Contact Mark Davison (Prof.) Links Contact the organisation Opens in new window Website Opens in new window Total cost No data Beneficiaries (1) Sort alphabetically Sort by EU Contribution Expand all Collapse all TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY Israel EU contribution € 1 453 802,00 Address SENATE BUILDING TECHNION CITY 32000 Haifa See on map Activity type Higher or Secondary Education Establishments Principal investigator Elad Eliezer Hazan (Dr.) Administrative Contact Mark Davison (Prof.) Links Contact the organisation Opens in new window Website Opens in new window Total cost No data